Knowledge catalogue
arXiv:2609.29581v1 Announce Type: new Abstract: Although diffusion-based methods have substantially improved the controllability of multimodal face synthesis, their semantic alignment remains suboptim
arXiv:2609.28836v1 Announce Type: cross Abstract: While various multimodal methods combining imaging and tabular data for Alzheimer's disease (AD) diagnosis were proposed, they are often limited in ge
arXiv:2609.28660v1 Announce Type: new Abstract: Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents chall
arXiv:2603.10287v2 Announce Type: replace-cross Abstract: LLM-as-a-Judge is a flexible framework for text evaluation, which allows us to obtain scores for the quality of a given text from various pers
arXiv:2609.30050v1 Announce Type: new Abstract: We present NNV3, the latest version of the Neural Network Verification (NNV) tool, a MATLAB framework for formal verification of deep learning models an
arXiv:2609.29142v1 Announce Type: cross Abstract: Direct On-Policy Distillation (Direct-OPD) transfers reinforcement-learning-induced policy improvements from a small model to a larger student by usin
arXiv:2609.28878v1 Announce Type: new Abstract: Sim-to-real transfer has made substantial progress, but can still produce controllers that remain stable and functional on hardware while suffering from
arXiv:2609.29788v1 Announce Type: new Abstract: Despite recent advancements in 3D generation, models often struggle to produce assets with high visual fidelity. To bridge this gap, we propose OREO, an
arXiv:2609.28967v1 Announce Type: new Abstract: Passive long-wave infrared (LWIR) hyperspectral ranging enables distance estimation in low-light and nighttime scenes by exploiting atmospheric absorpti
arXiv:2609.28547v1 Announce Type: new Abstract: Policy interventions propagate through public communication, institutional decisions, and stakeholder responses, yet datasets for financial multi-agent
arXiv:2609.28854v1 Announce Type: cross Abstract: Language-model agents increasingly answer questions over customer-relationship management (CRM) records, such as whether to qualify a sales lead. We i
arXiv:2609.30226v1 Announce Type: cross Abstract: Foundation models are post-trained with reinforcement learning (RL) to maximize specific rewards, such as human alignment, correctness, or instruction
arXiv:2609.28737v1 Announce Type: cross Abstract: Biological agents do not learn under conditions of unlimited computation. For humans, learning and choice are shaped by constraints on perception, att
arXiv:2609.29001v1 Announce Type: new Abstract: Despite strong performance on standard benchmarks, it remains unclear whether large language models (LLMs) evaluate social pragmatics in ways that align
arXiv:2609.29760v1 Announce Type: new Abstract: Humans typically rely on vision, touch, hearing, and proprioception to perceive contact and adapt their actions during manipulation. Providing robots wi
arXiv:2609.29458v1 Announce Type: new Abstract: We present a tightened convergence analysis of clipped gradient descent on (L_0, L_1)-smooth functions, with quantitative constants. Building on the ide
arXiv:2609.29690v1 Announce Type: new Abstract: University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social f
arXiv:2609.30023v1 Announce Type: new Abstract: Imitation learning enables robots to acquire manipulation skills from demonstrations, but the resulting policies can fail outside the training data, whi
arXiv:2609.28778v1 Announce Type: cross Abstract: Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding. On-
arXiv:2609.30177v1 Announce Type: new Abstract: Query understanding (QU) plays a critical role in production search systems, translating raw user queries into search execution plans that drive downstr
arXiv:2609.28756v1 Announce Type: cross Abstract: Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representat
arXiv:2609.30092v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models demonstrate impressive capabilities in robotic manipulation, their memoryless nature renders them brittle to
arXiv:2609.29822v1 Announce Type: new Abstract: Robotic imitation learning often relies on external cameras, yet local interaction cues such as object proximity, contact onset, and grasp state are dif
arXiv:2609.30238v1 Announce Type: new Abstract: Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to in
arXiv:2609.28582v1 Announce Type: cross Abstract: The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predict
arXiv:2609.28969v1 Announce Type: new Abstract: While end-to-end autonomous driving systems show promise, their application to micromobility vehicles is hindered by simulators failing to capture speci
arXiv:2609.29031v1 Announce Type: new Abstract: Torque observations in reinforcement learning remain challenging because simulated and measured torque differ in scale, offset, and noise. In this paper
arXiv:2609.29050v1 Announce Type: new Abstract: Tool-calling agents produce heterogeneous outputs, interleaving structured tool invocations with user-facing natural language summaries. This output het
arXiv:2609.29550v1 Announce Type: new Abstract: Optical satellite image correlation measures wide-area deformation in regimes where coherent interferometric synthetic aperture radar fails because disp
arXiv:2609.28979v1 Announce Type: new Abstract: We study spectral graph neural networks built from Hermite polynomials and propose HermNet, a simple model that combines a nodewise predictor with norma
Spot on. 'When we got attacked, our team initially turned to frontier closed-source APIs that blocked us because of safeguards that still can’t always tell the difference between attackers and defende
arXiv:2507.22170v2 Announce Type: replace-cross Abstract: Modern data analysis increasingly requires identifying shared latent structure across multiple high-dimensional datasets. A commonly used mode
arXiv:2609.28959v1 Announce Type: new Abstract: Humanoid parkour policies can traverse various terrains, but task completion may mask challenges of harsh landings, edge contacts, and unstable stance c
arXiv:2609.29131v1 Announce Type: cross Abstract: Internet texts are replete with format tags that carry structural, semantic, and functional meaning. Current large language model (LLM)-based translat
arXiv:2609.28739v1 Announce Type: new Abstract: Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized,
arXiv:2609.30210v1 Announce Type: new Abstract: Layer-wise visual-text similarity in Multimodal Large Language Models (MLLMs) is widely interpreted as evidence that the language model progressively in
arXiv:2609.29730v1 Announce Type: new Abstract: Bias in AI systems is typically framed as a flaw to be minimized, yet it also serves as a critical indicator of underlying weaknesses in data, modeling
arXiv:2609.29530v1 Announce Type: new Abstract: Validating a model on a time series asks for three things at once: each training run should use most of the sample (sufficiency), the test sets should t
arXiv:2609.29674v1 Announce Type: cross Abstract: Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an
arXiv:2609.29664v1 Announce Type: new Abstract: Reinforcement learning (RL) has proven effective in enhancing the reasoning performance of large language models (LLMs), particularly in complex mathema
arXiv:2609.30134v1 Announce Type: new Abstract: Neural behavior cloning compresses demonstrations into large models, making individual actions difficult to trace and policy updates costly. Retrieval p
arXiv:2609.29405v1 Announce Type: cross Abstract: We adapt Reinforce Adjoint Matching (RAM), a reward-based post-training method, to generative speech enhancement (SE). Starting from a pretrained SE m
arXiv:2609.29633v1 Announce Type: cross Abstract: We present TTLab's submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as to
At Google Cloud, we are committed to delivering the best managed experience backed by open source software. Today, we’re announcing the general availability of Memorystore for Valkey 9.1, which achiev
arXiv:2609.29118v1 Announce Type: new Abstract: LiDAR place recognition is a key front end for loop closure and global relocalization, yet indoor retrieval remains difficult when attitude or sensor mo
arXiv:2609.28792v1 Announce Type: new Abstract: Robust average-reward Markov decision processes provide a fundamental framework for long-term performance optimization under uncertainty, and can have o
arXiv:2609.29547v1 Announce Type: cross Abstract: Agentic AI is sold on a simple promise: the system keeps acting when the user stops watching. That promise creates an accountability inversion. As ste
arXiv:2609.29189v1 Announce Type: new Abstract: Scalable oversight aims to verify the behaviour of agents whose capabilities exceed those of their overseers. AI debate has been proposed as an oversigh
arXiv:2609.29387v1 Announce Type: new Abstract: Supervised synthetic CT (sCT) generation is commonly trained and evaluated as voxel-wise regression against registered reference CT images. In practice,
arXiv:2609.29095v1 Announce Type: cross Abstract: When a tool-using agent's write times out or returns a server error, the action may already have taken effect. Retrying blindly duplicates it -- a sec
arXiv:2609.29964v1 Announce Type: cross Abstract: General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them
1. fully agree w @JeffLadish that AI companies aren’t ready to handle superintelligent AI. 2. OpenAI hasn’t even handled the current breed of agents (far from superintelligent) well. 3. the US governm
arXiv:2609.27729v1 Announce Type: cross Abstract: In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or a
arXiv:2609.27180v1 Announce Type: cross Abstract: Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up. S
arXiv:2609.27753v1 Announce Type: new Abstract: Vision-language-action (VLA) models have become a powerful paradigm for generalist robotic manipulation, yet they are often reactive: the policy maps th
arXiv:2609.27269v1 Announce Type: new Abstract: Humanoid kicking requires coordinated whole-body motion and precise contact, while a banana kick demands contact mechanics that generate ball spin and a
arXiv:2609.27450v1 Announce Type: cross Abstract: Vision-language-action (VLA) models handle long-horizon manipulation, yet success hinges on a few precision-critical phases where millimeter-scale err
arXiv:2609.27513v1 Announce Type: new Abstract: Autoregressive robot policies learn continuous control by predicting discrete action tokens from observations. Different tasks often share local motions
arXiv:2608.24521v3 Announce Type: replace Abstract: Proactive medical dialogue requires an agent to decide what to ask from incomplete patient information. Existing information-seeking approaches comm
arXiv:2609.28281v1 Announce Type: new Abstract: Interlocking brick assembly provides a representative testbed for evaluating real-world robotic manipulation capabilities, where diverse structural desi